City Research Online

Abstract

The potential for machine learning systems to improve via a mutually beneficial exchange of information with users has yet to be explored in much detail. Previously, we found that users were willing to provide a generous amount of rich feedback to machine learning systems, and that the types of some of this rich feedback seem promising for assimilation by machine learning algorithms. Following up on those findings, we ran an experiment to assess the viability of incorporating real-time keyword-based feedback in initial training phases when data is limited. We found that rich feedback improved accuracy but an initial unstable period often caused large fluctuations in classifier behavior. Participants were able to give feedback by relying heavily on system communication in order to respond to changes. The results show that in order to benefit from the user’s knowledge, machine learning systems must be able to absorb keyword-based rich feedback in a graceful manner and provide clear explanations of their predictions.

Publication Type:	Conference or Workshop Item (Paper)
Additional Information:	© Stumpf, S.| ACM 2008. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in Proceedings of the 13th international conference on Intelligent user interfaces, http://dx.doi.org/10.1145/1378773.1378781.
Publisher Keywords:	Machine learning, user feedback
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Departments:	School of Science & Technology > Computer Science > Human Computer Interaction Design

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